Bridging the Gap: Exploring the Potential for Community-based Watershed Monitoring to Enhance Ecosystem Health and Watershed Governance in Canada
Bibliographic record
Abstract
Watershed monitoring is an essential component of watershed management; however, widespread federal and provincial decentralization efforts have resulted in reduced government funding for such monitoring. In response, communities are mobilizing to address this deficit in Canada by undertaking a practice called community-based watershed monitoring (CBWM). Although CBWM is being employed to address this gap, monitoring data collected by CBWM organizations remains underutilized by decision-makers in watershed governance. Moreover, CBWM organizations face significant challenges with knowledge exchange due to a lack of rigorous scientific protocols and high organizational turnover. At the same time, decision-makers are experiencing minimal capacity to utilize CBWM data due to restricted mandates and resources. Nonetheless, research suggests that communities significantly benefit from CBWM, but less evidence exists to confirm effects of CBWM activities on ecosystem health and there is scant literature about successful CBWM data integration. Anecdotal evidence regarding ecosystem benefits provided by CBWM exists in grey literature and on websites; however, more peer-reviewed literature must be established to support these claims. Uncertainty still remains regarding how to track the success of CBWM and watershed restoration efforts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".